Magaña Lizarrondo, Eduardo
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Magaña Lizarrondo
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Eduardo
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Ingeniería Eléctrica, Electrónica y de Comunicación
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ISC. Institute of Smart Cities
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Publication Open Access Interactivity anomaly detection in remote work scenarios using LTSM(IEEE, 2024) Arellano Usón, Jesús; Magaña Lizarrondo, Eduardo; Morató Osés, Daniel; Izal Azcárate, Mikel; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISCIn recent years, there has been a notable surge in the utilization of remote desktop services, largely driven by the emergence of new remote work models introduced during the pandemic. These services cater to interactive cloud-based applications (CIAs), whose core functionality operates in the cloud, demanding strict end-user interactivity requirements. This boom has led to a significant increase in their deployment, accompanied by a corresponding increase in associated maintenance costs. Service administrators aim to guarantee a satisfactory Quality of Experience (QoE) by monitoring metrics like interactivity time, particularly in cloud environments where variables such as network performance and shared resources come into play. This paper analyses anomaly detection state of the art and proposes a novel system for detecting interactivity time anomalies in cloud-based remote desktop environments. We employ an automatic model based on LSTM neural networks that achieves an accuracy of up to 99.97%.Publication Open Access KISS methodologies for network management and anomaly detection(IEEE, 2018) Vega, Carlos; Aracil Rico, Javier; Magaña Lizarrondo, Eduardo; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenCurrent networks are increasingly growing in size, complexity and the amount of monitoring data that they produce, which requires complex data analysis pipelines to handle data collection, centralization and analysis tasks. Literature approaches, include the use of custom agents to harvest information and large data centralization systems based on clusters to achieve horizontal scalability, which are expensive and difficult to deploy in real scenarios. In this paper we propose and evaluate a series of methodologies, deployed in real industrial production environments, for network management, from the architecture design to the visualization system as well as for the anomaly detection methodologies, that intend to squeeze the vertical resources and overcome the difficulties of data collection and centralization.